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---
language: en
license: mit
dataset_info:
features:
- name: _id
dtype: string
- name: sentence
dtype: string
- name: target
dtype: string
- name: aspect
dtype: string
- name: score
dtype: float64
- name: type
dtype: string
splits:
- name: train
num_bytes: 119567
num_examples: 822
- name: valid
num_bytes: 17184
num_examples: 117
- name: test
num_bytes: 33728
num_examples: 234
download_size: 102225
dataset_size: 170479
---
# Dataset Name
## Dataset Description
This dataset is based on the task 1 of the Financial Sentiment Analysis in the Wild (FiQA) challenge. It follows the same settings as described in the paper 'A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News'. The dataset is split into three subsets: train, valid, test with sizes 822, 117, 234 respectively.
## Dataset Structure
- `_id`: ID of the data point
- `sentence`: The sentence
- `target`: The target of the sentiment
- `aspect`: The aspect of the sentiment
- `score`: The sentiment score
- `type`: The type of the data point (headline or post)
## Additional Information
- Homepage: [FiQA Challenge](https://sites.google.com/view/fiqa/home)
- Citation: [A Baseline for Aspect-Based Sentiment Analysis in Financial Microblogs and News](https://arxiv.org/pdf/2211.00083.pdf)
## Downloading CSV
```python
from datasets import load_dataset
# Load the dataset from the hub
dataset = load_dataset("ChanceFocus/fiqa-sentiment-classification")
# Save the dataset to a CSV file
dataset["train"].to_csv("train.csv")
dataset["valid"].to_csv("valid.csv")
dataset["test"].to_csv("test.csv")
```
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